Soil Salinity Study in Northern Great Plains Sodium Affected Soil

Climate and land-use changes when combined with the marine sediments that underlay portions of the Northern Great Plains have increased the salinization and sodification risks. The objectives of this dissertation were to compare three chemical amendments (calcium chloride, sulfuric acid and gypsum) remediation strategies on water permeability and sodium (Na) transport in undisturbed soil columns and to develop a remote sensing technique to characterize salinization in South Dakota soils. Fortyeight undisturbed soil columns (30 cm x 15 cm) collected from White Lake, Redfield, and Pierpont were used to assess the chemical remediation strategies. In this study the experimental design was a completely randomized design and each treatment was replicated four times. Following the application of chemical remediation strategies, 45.2 cm of water was leached through these columns. The leachate was separated into 120- ml increments and analyzed for Na and electrical conductivity (EC). Sulfuric acid increased Na leaching, whereas gypsum and CaCl2 increased water permeability. Our results further indicate that to maintain effective water permeability, ratio between soil EC and sodium absorption ratio (SAR) should be considered. In the second study, soil samples from 0-15 cm depth in 62 x 62 m grid spacing were taken from the South Dakota Pierpont (65 ha) and Redfield (17 ha) sites. Saturated paste EC was measured on each soil sample. At each sampling points reflectance and derived indices (Landsat 5, 7, 8 images), elevation, slope and aspect (LiDAR) were extracted. Regression models based on multiple linear regression, classification and regression tree, cubist, and random forest techniques were developed and their ability to predict soil EC were compared. Results showed that: 1) Random forest method was found to be the most effective method because of its ability to capture spatially correlated variation, 2) the short wave infrared (1.5 -2.29 μm) and near infrared (0.75-0.90 μm) were very sensitive to soil salinity; 3) EC prediction model using all 3 season (spring, summer and fall) images was better on state wide validation dataset compared to individual season model. Finally, in eastern South Dakota, the model predicted that from 2008 to 2012, EC increased in 569,165 ha or 13.4% of the land seeded to corn (Zea mays L.) or soybeans (Glycine max L)

Assessing wind damage and potential yield loss in mid-season corn using a geospatial approach

Yield loss due to natural disasters, such as storms with high-speed winds and rainfall, can significantly damage standing corn (Zea mays L.) plants and yield. Using a geospatial approach, the study aimed to estimate green snap wind damage to corn and assess potential yield and economic loss in the Mississippi Delta. Midseason corn (V12–V14) snapping occurred on 8 June 2022. We recorded green snap damage in 13 fields [1.0 to 2.0 hectares (ha−1)] with low (224 kg ha−1) and high (336 kg ha−1) N rates and two different row orientations (north–south and east–west) after the damage. The results indicated no nitrogen rates or row orientation effect on green snap damage. The average yield loss could be ~29.25 kg ha−1, with every 1% increase in green snap wind damage causing significant economic loss to producers. Research methods can help scientists to estimate potential green snap yield loss due to severe winds in the larger fields. Research results can also help estimate potential yield and economic loss to assist producers and other stakeholders in decision-making to prepare for changing weather patterns and unprecedented severe windstorms in the future

Do Precision Chemical Amendment Applications Impact Sodium Movement in Dryland Semiarid Saline Sodic Soils?

Expanding sodicity and salinity problems have placed many northern Great Plains (NGP) soils at the sustainability tipping point. This study assessed the impact of chemical restoration on water and salt transport in undisturbed soil columns collected from three hillslope model landscape positions. The backslope (Redfield), footslope (White Lake), and toeslope (Pierpont) soils had moderate (3.27 ± 0.59), high (7.3 ± 3.34), and very high (13.29 ± 3.2) sodium adsorption ratio (SARe) values, respectively. The soils were treated with KBr and one of four soil amendments (none, H2SO4, CaSO4, and CaCl2). The rapid movement of Br−through the columns suggested that bypass water flow occurred. In addition, a comparison with widely used salinity models (final EC = 0.8 × initial EC/pore volume [PV]) underestimated the leaching requirements by 69, 79, and 41% in the backslope, footslope, and toeslope soils. In the footslope soils with high SAR values, H2SO4 was more effective at promoting Na+leaching than gypsum or CaCl2. However, in back slope and toeslope soils with moderate and very high SAR values, the chemical amendments were not, and were equally effective at facilitating Na+ leaching, respectively. These findings suggest that chemical amendments should target treatments to problem areas, and that bypass flow can influence their effectiveness. The LOESS regression model suggested that the electrical conductivity (ECe)/SARe ratio was useful for assessing Na+ risks, and that to maintain a water flow rate of 1 mm h–1 in a soil with a SARe value of 1, an ECe value of ≥2 was required

Winter Wheat Quality Responses to Water, Environment, and Nitrogen Fertilization

Decreasing carbon (C) footprints by reducing nitrogen (N) and water inputs has been speculated to have negative impacts on wheat grain yield and flour processing quality. The objective of this study was to determine the impact of N and water stress on winter wheat grain yield, protein composition, and dough quality. Wheat fertilized at two N rates (unfertilized and recommended) was grown under water-stressed and well-watered environments. Nitrogen and water stress were measured using the 13C isotopic approach. Research showed that (1) N fertilizer and the water-management environment produced similar impacts on wheat quality and yield loss due to N stress and yield loss due to water stress (YLWS); (2) N fertilizer increased flour protein, dough stability, and relative concentration of glutenin (%Glu), unextractable polymeric protein (UPP), and relative amount of high-molecular-weight glutenin subunits (HMW-GS/LMW-GS); (3) the well-watered environment reduced protein contents when N mineralization was low, whereas it did not influence protein content when mineralization was high; and (4) the %Glu was negatively correlated with yield loss due to N stress (YLNS) and positively correlated with stability. This study showed that a clear understanding of the complex relationship between soil variability and climatic conditions should make it possible to develop adaptive management practices, increase profitability, and improve quality

Soil and Land-Use Change Sustainability in the Northern Great Plains of the USA

In the Northern Great Plains (NGP), the combined impacts of land-use and climate variability have the potential to place many soils on the tipping point of sustainability. The objectives of this study were to assess if the conversion of grassland to croplands occurred on fragile landscapes in the North America Northern Great Plains. South Dakota and Nebraska were selected for this study because they are located in a climate transition zone. We visually classified 43,200 and 38,400 points in South Dakota and Nebraska, respectively, from high-resolution imagery in 2006, 2012, and 2014 into five different categories (cropland, grassland, habitat, NonAg, and water). The sustainability risk of the land-use changes was assessed based on the land capability class (LCC) scores at the selected sites. Sites with LCC scores ≤ 4 are considered sustainable for crop production if appropriate management practices are followed. Scores ≥ 6 are not considered suitable for row crop production. From 2006 to 2014, 910,000 and 360,000 ha of land were converted from grassland to cropland in South Dakota and Nebraska, respectively. Approximately 92 and 80% of the grassland conversion to croplands occurred on land suitable for crop production (land capability class, LCC ≤ 4) in South Dakota and Nebraska, respectively

Linking and Sharing Technology: Partnerships for Data Innovations for Management of Agricultural Big Data

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges

Using simulated rainfall to evaluate cover crops and winter manure application to limit nutrient loss in runoff

Abstract Cover crops can be effective in minimizing nutrient losses from agricultural fields. The objective of this study was to determine the impact of cover crop (rye, Secale cereale L.) and winter manure application on nutrient loss in simulated rainfall runoff. A split block design study with manure (as vertical block) and cover crops (as horizontal block) was established in 2009. Two rain simulations (the first defined as “dry” and the second “wet”) using sixteen 4 m2 steel frames were conducted in May 2010. The runoff volume collected from each plot was analyzed for nitrate–nitrogen (NO3–N), total suspended solids, total Kjeldahl nitrogen, total phosphorus, and total dissolved phosphorus. In the dry run, the concentration and load of NO3–N were significantly lower (p = 0.05) in runoff with the cover crop than in no‐cover crop treatment. Overall, cover crops reduced nutrient loss in concentration by 6%–48% in the dry and 8%–40% in the wet run than with no‐cover crops. The concentration and load of NO3–N were significantly higher under manure treatments in both “dry” and “wet” runoff runs compared to no‐manure application. Manure application increased nutrient loss in concentration by 6%–58% in the dry and 10%–69% in the wet run than with no‐manure application. This study helps us to understand the complexity of winter manure application with cover crops and potential risks of nutrient loss to surface runoff during spring in the Northern Great plains of the Dakotas

Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system

Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species

Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species